Supervised learning of visual affordances for autonomous driving was introduced by [1]}, though they use the predicted affordances to plan using a set of fixed rules whereas our network uses visual affordances as auxiliary tasks for the main task of driving. Action primitives can be inferred as sub-policies for the desired task. Learning hierarchical policies via demonstration is an active area of research [2]}, [3]}, [4]}, [5]} and research in developmental psychology has also found evidence of hierarchical task decomposition during imitation in young children [6]}. Our work decomposes the main task of driving into sub-policies which are used as auxiliary supervision to derive the final control commands.
